General research interests and goals:

Our group at the Department of Theoretical Neuroscience, Central Institute of Mental Health, follows two main, closely related research objectives:1) We construct highly data-driven neuro-computational/ mathematical models of the neuronal dynamics underlying higher brain functions, like rule learning, working memory, or cognitive flexibility, and the distortions of these processes in psychiatric conditions like schizophrenia or depression. ‘Highly data-driven’ in this context means that these models are systematically derived or inferred from in-vitro and in-vivo experimental data in a statistically principled way (e.g., by the method of maximum-likelihood; see Durstewitz et al., 2016). These models are then used to investigate the dynamical underpinnings and mechanisms of psychiatric disorders.2) We develop novel statistical and machine learning approaches for neural data analysis, in particular the analysis of high-dimensional (multivariate) time series as generated by neuroimaging or multiple spike train recording techniques. These methods are designed to either reconstruct from the observed time series the underlying neuronal dynamics (e.g., Balaguer-Ballester et al. 2011; Lapish et al. 2015), i.e. properties like attractor states or phase transitions, or to dig for spatio-temporal patterns within the time series data across multiple time scales (e.g., Russo & Durstewitz 2017).